CN113820227A - Dynamic self-organizing health monitoring method - Google Patents
Dynamic self-organizing health monitoring method Download PDFInfo
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- CN113820227A CN113820227A CN202110968033.5A CN202110968033A CN113820227A CN 113820227 A CN113820227 A CN 113820227A CN 202110968033 A CN202110968033 A CN 202110968033A CN 113820227 A CN113820227 A CN 113820227A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/30—Investigating strength properties of solid materials by application of mechanical stress by applying a single impulsive force, e.g. by falling weight
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
- G01B11/18—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge using photoelastic elements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
- G01M7/08—Shock-testing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/0058—Kind of property studied
- G01N2203/006—Crack, flaws, fracture or rupture
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/06—Indicating or recording means; Sensing means
- G01N2203/067—Parameter measured for estimating the property
- G01N2203/0682—Spatial dimension, e.g. length, area, angle
Abstract
The invention discloses a dynamic self-organizing health monitoring method, which specifically comprises the following steps: step 1, establishing a knowledge body according to a monitored object; step 2, storing the knowledge body into a MYSQL database to complete connection with a LabVIEW upper computer; and 3, monitoring and diagnosing the monitoring condition of the dynamic self-organization in a LabVIEW upper computer based on the knowledge body established in the step 1. The invention solves the problem that the tissue damage of the monitored object cannot be comprehensively monitored in the traditional technology.
Description
Technical Field
The invention belongs to the technical field of health management and monitoring of structural damage, and relates to a dynamic self-organizing health monitoring method.
Background
The health management technology is characterized in that the state characteristics of a monitored object are obtained in real time through online monitoring, advanced signal analysis, processing and diagnosis methods and some advanced artificial intelligence methods are effectively managed, and the resources are optimally configured to evaluate and predict the running state of the equipment, so that the monitoring, detection and evaluation methods of mechanical products are integrated, and the safe running of the equipment in the whole life is ensured. When the equipment system is defective, if the defects cannot be found in time, the service life of the component is shortened seriously, and safety accidents happen seriously, so that the health management technology has a remarkable effect on improving the safety and the economy of the health supervision field of structural damage, and therefore, the health management of the structural damage is very necessary.
The traditional health management method adopts a single monitoring management mode, a monitoring system is simple, various injuries cannot be monitored simultaneously, and historical injury information of a structure cannot be checked at a later stage. In order to make up for the defects of the traditional technology, a dynamic self-organizing health management method is provided.
Disclosure of Invention
The invention aims to provide a dynamic self-organizing health monitoring method, which solves the problem that the tissue damage of a monitored object cannot be comprehensively monitored in the traditional technology.
The invention adopts the technical scheme that the dynamic self-organizing health monitoring method specifically comprises the following steps:
step 1, establishing a knowledge body according to a monitored object;
step 2, storing the knowledge body into a MYSQL database to complete connection with a LabVIEW upper computer;
and 3, monitoring and diagnosing the monitoring condition of the dynamic self-organization in a LabVIEW upper computer based on the knowledge body established in the step 1.
The invention is also characterized in that:
in step 1, the knowledge body comprises an object module, a signal acquisition module, a signal processing module, a fault diagnosis module and a display module.
The object attributes of each module in the knowledge ontology are all in a single directional, fixed and unique relationship.
The specific process of the step 2 is as follows:
step 2.1, establishing a new Prot é g e database in the installed MYSQL, checking whether the MYSQL is running under a DOS window, establishing a new database named protegeone after connection is successful, confirming that the MYSQL is running again after the establishment is finished, downloading a JDBC driver, connecting the Prot e g e and the database, and storing a knowledge body in the MYSQL database according to a filled user name and a password in a popped dialog box;
step 2.2: inputting a user name and a password in native for mysql software to establish connection with a protegeone database, exporting database information into an excel form, and reading the exported database excel file by LabVIEW to realize connection between the database and an upper computer.
The specific process of the step 3 is as follows:
step 3.1, selecting an instance of an object module through a central management module in the LabVIEW upper computer, and reading user data through the object module; the method comprises the steps that a signal acquisition module is used for receiving data of an object module, and when the timing of a sensor in the signal acquisition module reaches time t, the data in the signal acquisition module are sent to a signal processing module;
step 3.2, processing the received data by adopting Gabor wavelet transform in the signal processing module to obtain characteristic parameters of the structural damage, setting the Boolean control to be 'Ture' when all the characteristic parameters of the structural damage are identified, sending the characteristic parameters to the fault diagnosis module, setting the 'Ture' to the Boolean control after the Boolean control is received in the fault diagnosis module, stopping while circulation, and performing fault diagnosis; the fault diagnosis algorithm adopts a judgment algorithm based on data;
and 3.3, when the operation of the fault diagnosis scheme in the fault diagnosis module is finished, setting the Boolean control as 'Ture', and sending the diagnosis result to the display module to display the judgment result.
The invention has the beneficial effects that in the configuration system of the designed knowledge base, the system consists of two parts and is executed in a sequential mode, and the first part is matched with each module in the database and returns the position information of the modules. The second part is to determine the result of the solution arrangement with respect to the position information. The diagnosis database can store the health management diagnosis data of each time into the database, so that later-stage workers can complete the integration of the health management data, the query of the knowledge body, the query of historical fault diagnosis results and the optional addition or deletion of the health management diagnosis results, and the system integration of the health management data is realized. When a communication protocol based on the DataSocket technology is utilized, because the DataSocket address is named by a module name and the module is a conditional branch structure, the data processed can be transmitted to the next module no matter what scheme is executed by the module, and the DPM module can display the result of health management in the health management system without being influenced. Therefore, the scheme chain is conveniently added in the structural health management system in the later period, and the function of dynamically configuring the scheme in the structural health management system is realized.
Drawings
FIG. 1 is a system architecture diagram of a dynamic self-organizing health monitoring method of the present invention;
FIG. 2 is a flow diagram of a knowledge database based protocol configuration system process for a dynamic ad hoc health monitoring method of the present invention;
fig. 3 is a branch diagram of the modules of the upper computer program according to the embodiment of the dynamic self-organizing health monitoring method of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses a dynamic self-organizing health monitoring method, which comprises the following steps:
step 1: establishing a corresponding knowledge body according to the monitored object, see fig. 1;
step 1.1, establishing a knowledge body by using Prot é software, wherein a concept class main body comprises an OM (object module), an SM (sensor module), an SPM (signal processing module), a DM (diagnostic module, fault diagnosis module) and a DPM (display module), determining that the object attributes of each knowledge body are all in a single-pointing type, fixed and unique relationship, and determining the pointing relationship between each scheme of each module;
step 2, storing the knowledge body into a MYSQL database to complete connection with a LabVIEW upper computer;
step 2.1, establishing a new Prot é g e database in the installed MYSQL, checking whether the MYSQL is running under a DOS window, establishing a new database named protegeone after connection is successful, confirming that the MYSQL is running again after establishment is completed, downloading a JDBC driver, connecting the Prot e g e database and the database, and filling information such as a user name and a password in a popped dialog box according to requirements to successfully store a knowledge body in the MYSQL database;
step 2.2: inputting a user name and a password in native for mysql software to establish connection with a protegeone database, seeing database information in the software and exporting the database information into an excel form, and reading exported database excel files by LabVIEW to realize connection between the database and an upper computer;
step 3, configuring each module scheme based on the knowledge database, and completing the scheme configuration of each module according to reasoning, referring to FIG. 2;
step 3.1, selecting an example of an OM module by a central management module in the upper computer, and reading user data through the OM module; receiving data of an OM module by using an SM module (the arrangement of sensors in the SM module mainly comprises the following modes that an optical fiber sensor forms a sensor network for strain data acquisition, intelligent wireless sensors mutually communicate by adopting the same communication protocol to form a mutually interactive network), and when the timing reaches 2s, transmitting the data to an SPM module for next signal processing according to the arrangement of the sensor network;
step 3.2, processing the received bottom data in the SPM module by utilizing signal processing modes such as Gabor wavelet transform, FFT and the like to obtain characteristic parameters of the structural damage, setting the Boolean control to be 'Ture' when the operation of the mode identification scheme in the signal processing module of the structural damage object is finished, sending the characteristic data to the DM module, setting the 'Ture' in the Boolean control received in the fault diagnosis processing module, stopping while circulation and performing fault diagnosis; the fault diagnosis algorithm uses a data-based judgment algorithm, such as a BP neural network, a wavelet neural network, a deep learning network, and the like.
And 3.3, when the operation of the fault diagnosis scheme in the fault diagnosis module is finished, setting the Boolean control to be 'Ture', setting the Boolean control of the fault diagnosis scheme in the previous module in the display module to be 'Ture', stopping while circulation, stepping into the structural damage display scheme in the execution sequence structure display module, diagnosing the fault mode by using different fault diagnosis algorithms, receiving the diagnosed numerical value, comparing the diagnosed numerical value with a set threshold value, considering that the fault mode is in the fault mode when the diagnosis data of the structural damage form of the fault mode is greater than the set threshold value, and sending the diagnosis result to the DPM module to display the judgment result.
Examples
The method comprises the following steps of (1) adopting a common aviation aluminum plate of an airplane as an experimental material, generating impact in a mode that a pencil lead is broken at an impact point, executing step 1, firstly establishing a knowledge body with structural damage of 'impact' in prot gee software, and determining that the attribute between each module is a single-direction type relation; see fig. 3;
step 2 is executed, normal operation of MYSQL is checked under a DOS window, a new Prot g e database is established, after the establishment is completed, the MYSQL is confirmed to be operated again, a driver is downloaded, the Prot g e database is connected with the database, a user name of 123 and a password bit of 456 are filled according to requirements, then a knowledge body can be successfully stored in the MYSQL database in an impacting mode, a user name and a password are input in native for MYSQL software, connection with the protegeone database can be established, the connection with the protegeone database can be exported to be in an excel mode, and connection between the database and an upper computer is achieved through LabVIEW reading files;
and 3, selecting the example of the OM module as cj through the central management module, reading user data by the OM, sending the data to the SM module, wherein the arrangement mode of the sensors is multi-array arrangement, when the time delay reaches 2s, transmitting the data to the SPM module, extracting the characteristic parameters of the damage by using a Matbal script node in the SPM module, extracting the time difference corresponding to the maximum peak value of the envelope curve of the signal after the signal and gabor wavelet transformation as the characteristic parameters, setting the Boolean control as 'Ture', sending the characteristic parameters to the DM module, performing fault diagnosis by using a BP neural network algorithm in the DM module, judging the fault with a set threshold, setting 'Ture' in the Boolean control if the time difference is greater than the set threshold, and sending the diagnosis result to the DPM module to display the judgment result.
Claims (5)
1. A dynamic self-organizing health monitoring method is characterized in that: the method specifically comprises the following steps:
step 1, establishing a knowledge body according to a monitored object;
step 2, storing the knowledge body into a MYSQL database to complete connection with a LabVIEW upper computer;
and 3, monitoring and diagnosing the monitoring condition of the dynamic self-organization in a LabVIEW upper computer based on the knowledge body established in the step 1.
2. The dynamic ad hoc health monitoring method of claim 1, wherein: in the step 1, the knowledge body comprises an object module, a signal acquisition module, a signal processing module, a fault diagnosis module and a display module.
3. The dynamic ad hoc health monitoring method of claim 2, wherein: the object attributes of the modules in the knowledge ontology are all in a single directional, fixed and unique relationship.
4. The dynamic ad hoc health monitoring method of claim 2, wherein: the specific process of the step 2 is as follows:
step 2.1, establishing a new Prot é g e database in the installed MYSQL, checking whether the MYSQL is running under a DOS window, establishing a new database named protegeone after connection is successful, confirming that the MYSQL is running again after the establishment is finished, downloading a JDBC driver, connecting the Prot e g e and the database, and storing a knowledge body in the MYSQL database according to a filled user name and a password in a popped dialog box;
step 2.2: inputting a user name and a password in native for mysql software to establish connection with a protegeone database, exporting database information into an excel form, and reading the exported database excel file by LabVIEW to realize connection between the database and an upper computer.
5. The dynamic ad hoc health monitoring method of claim 4, wherein: the specific process of the step 3 is as follows:
step 3.1, selecting an instance of an object module through a central management module in the LabVIEW upper computer, and reading user data through the object module; the method comprises the steps that a signal acquisition module is used for receiving data of an object module, and when the timing of a sensor in the signal acquisition module reaches time t, the data in the signal acquisition module are sent to a signal processing module;
step 3.2, processing the received data by adopting Gabor wavelet transform in the signal processing module to obtain characteristic parameters of the structural damage, setting the Boolean control to be 'Ture' when all the characteristic parameters of the structural damage are identified, sending the characteristic parameters to the fault diagnosis module, setting the 'Ture' to the Boolean control after the Boolean control is received in the fault diagnosis module, stopping while circulation, and performing fault diagnosis; the fault diagnosis algorithm adopts a judgment algorithm based on data;
and 3.3, when the operation of the fault diagnosis scheme in the fault diagnosis module is finished, setting the Boolean control as 'Ture', and sending the diagnosis result to the display module to display the judgment result.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120318925A1 (en) * | 2011-06-16 | 2012-12-20 | Space Administration | Multi-Dimensional Damage Detection |
| CN108757502A (en) * | 2018-05-15 | 2018-11-06 | 江苏大学 | A kind of water pump assembly typical case's health status monitoring device and method based on Internet of Things |
| CN109596162A (en) * | 2018-11-12 | 2019-04-09 | 华北水利水电大学 | Civil engineering structural remote health monitoring system |
| CN110177017A (en) * | 2019-06-04 | 2019-08-27 | 沃德(天津)智能技术有限公司 | A kind of speed reducer intelligent Fault Diagnose Systems and its diagnostic method |
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- 2021-08-23 CN CN202110968033.5A patent/CN113820227A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120318925A1 (en) * | 2011-06-16 | 2012-12-20 | Space Administration | Multi-Dimensional Damage Detection |
| CN108757502A (en) * | 2018-05-15 | 2018-11-06 | 江苏大学 | A kind of water pump assembly typical case's health status monitoring device and method based on Internet of Things |
| CN109596162A (en) * | 2018-11-12 | 2019-04-09 | 华北水利水电大学 | Civil engineering structural remote health monitoring system |
| CN110177017A (en) * | 2019-06-04 | 2019-08-27 | 沃德(天津)智能技术有限公司 | A kind of speed reducer intelligent Fault Diagnose Systems and its diagnostic method |
Non-Patent Citations (1)
| Title |
|---|
| 李宏男;高东伟;伊廷华;: "土木工程结构健康监测系统的研究状况与进展", 力学进展, no. 02, pages 2 - 6 * |
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